1. Field of the Invention
The present invention relates to a method for extracting a characteristic frequent pattern using a user's item operation history, and a method for recommending an item as well as an operation for that item to a user using that extraction method.
2. Description of the Related Art
Examples of conventional methods for a user to search for a desired item include a method in which matching is performed based on a keyword and a method in which the layers of a categorized and hierarchical item are narrowed down by selection. However, these conventional methods place a large burden on the user, and in a worst-case, depending on a user's technical level, a desired item may not be found. Thus, to reduce the user's workload, recommendation methods have been proposed that automatically search for necessary items and present the results to the user.
An example of a well-known recommendation method is collaborative filtering, which is widely used in the electronic commerce site (EC site). This method extracts similar users who have similar usage trends of an item based on the past usage history, and predicts an item to recommend using the usage history of those similar users.
However, desired items in an office are not only information used as an information resource for creating information, such as internal company documents and Web documents. Sought-after items may also be information about a procedure for achieving predetermined work, a method for efficiently proceeding with work, or some kind of know-how. Although it is desirable if such information is organized based on experience and explicitly stated as a workflow, if such information is not explicitly stated, a person has to spend time and effort to search for the information by asking somebody who is likely to know or learn for himself/herself through trial and error.
Accordingly, various technologies have been proposed to extract a partial data sequence that frequently appears from a usage history arranged in time series (sequential pattern mining), and utilize that partial data sequence as a recommendation for a characteristic pattern. For example, by analyzing a Web access log and extracting patterns such as page F is often viewed after page A is viewed, a recommendation can be made to a user who has viewed page A to then view page F. Thus, in cases in which the order of item usage is important, this type of recommendation method is widely proposed.
Japanese Patent No. 3860602 discusses services and information that have been specialized for an operator by extracting a frequent operation pattern from an operation history of one or more devices (mainly household appliances), and estimating who the operator is. A temporal set of an operation is referred to as an element. An operation that frequently appears among elements is extracted as a frequent operation pattern.
Further, a technology that searches for past jobs that are the same as the job currently being performed by using a usage history, and makes a recommendation based on the search result has also been proposed. Japanese Patent Application Laid-Open No. 2009-211385 discusses a technology in which a document previously used for a job and the usage state and edited content of the document in that job are stored as a job record, which is used to make a related document recommendation to the user. A highly accurate recommendation about related information that matches the job objective is made by extracting similar jobs in the past based on a degree of similarity of the operation content with the user's current document, and extracting the document used for that job. The analogy with past similar jobs is determined based on a degree of similarity of the operation content on a document whose document ID matched.
The basic methods for making such a recommendation like the conventional methods are as follows.
1. Extracting a characteristic frequent operation pattern beforehand from a usage history.
2. Estimating which frequent operation pattern the operation currently being performed belongs to based on the most recent operation by the user.
3. Making a recommendation to the user regarding the next operation based on the estimated frequent operation pattern.
However, when considering document usage in an office, there is a large problem when extracting the characteristic frequent operation pattern. Typically, even if workflows have a document operation flow that is generally the same, when looked at carefully, the order of operations is often random, and the operation target file names are often different. In such a case, not only is it difficult to find a frequently appearing pattern, but there is a high likelihood that the extracted operation pattern is not very versatile.
For example, in a case of a workflow for creating a quotation, even if the general operation flow is the same for two users, user A and user B, there is a high likelihood that the document name of the quotation to be created will be different, that the names of documents opened for reference will be different, and that the order for opening those documents will be different. Although this example describes differences between users, even the same user often makes slight changes to the operation content from time to time. Thus, it can be considered that discovering exactly the same frequent operation pattern is difficult. Even supposing that such a pattern could be found, it is highly likely that the operation pattern is overly specialized, and thus its suitability for estimating a workflow for the user's operations is reduced.